IVAICVROJun 30, 2025

SurgiSR4K: A High-Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures

arXiv:2507.00209v31 citationsh-index: 16Machine Learning for Biomedical Imaging
Originality Synthesis-oriented
AI Analysis

This dataset addresses a gap for researchers in surgical imaging and computer vision, enabling tasks such as super-resolution and instrument detection, but it is incremental as it primarily provides new data rather than novel methods.

The authors tackled the lack of publicly available native 4K datasets for robotic-assisted minimally invasive surgery by introducing SurgiSR4K, a high-resolution endoscopic video dataset that includes diverse visual scenarios like specular reflections and tool occlusions.

High-resolution imaging is crucial for enhancing visual clarity and enabling precise computer-assisted guidance in minimally invasive surgery (MIS). Despite the increasing adoption of 4K endoscopic systems, there remains a significant gap in publicly available native 4K datasets tailored specifically for robotic-assisted MIS. We introduce SurgiSR4K, the first publicly accessible surgical imaging and video dataset captured at a native 4K resolution, representing realistic conditions of robotic-assisted procedures. SurgiSR4K comprises diverse visual scenarios including specular reflections, tool occlusions, bleeding, and soft tissue deformations, meticulously designed to reflect common challenges faced during laparoscopic and robotic surgeries. This dataset opens up possibilities for a broad range of computer vision tasks that might benefit from high resolution data, such as super resolution (SR), smoke removal, surgical instrument detection, 3D tissue reconstruction, monocular depth estimation, instance segmentation, novel view synthesis, and vision-language model (VLM) development. SurgiSR4K provides a robust foundation for advancing research in high-resolution surgical imaging and fosters the development of intelligent imaging technologies aimed at enhancing performance, safety, and usability in image-guided robotic surgeries.

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